Abstract: Changes of vegetation cover directly determine local climate and ecological balance, particularly on environmental construction and land use. Western China is included in the Silk Road Economic Belt, accounting for more than half of the total land area of China, but its economic development is relatively backward. Much attention has paid to construct a friendly environment while developing economy, in order to achieve the sustainable development of the green economy in western China. It has become one of the urgent issues to clarify the influence of natural factors on vegetation cover, and thereby to take corresponding measures or policies. In this study, five provinces in northwest China and four provinces in southwest China were selected as the study area, where the complex and diverse characteristics of terrain and climate have shaped the unique spatial distribution pattern of vegetation cover. MOD13 Q1 and SRTM was accessed online, based on massive remote sensing data shared by Google Earth Engine (GEE) and powerful ground analysis capabilities. The monthly climate data set was from the China Meteorological Data (http://data.cma.cn/), while, the humidity index, cumulative temperature (≥10 ℃), soil type, vegetation type, and physiognomy type were all from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (http://www.resdc.cn/). The linear regression, Maximum Value Composite (MVC) and mean method were used to analyze the spatiotemporal variation of vegetation cover in provinces along the Silk Road Economic Belt in China during 2000-2018. A total of eight factors can be identified on the changes of vegetation cover, using factor detector and ecological detector in the geographical detector to screen out the annual average precipitation, humidity index, vegetation type, soil type, annual average temperature, physiognomy type, slope, and cumulative temperature (≥10 ℃). Risk detector was used to recognize the optimal range or behaviors of different natural factors, thereby to promote vegetation growth. interaction between different factors was detected by interaction detector. The results showed that: 1) The spatio-temporal variation trend in vegetation cover was significant from 2000-2018. Spatial variation of vegetation cover was remarkably improved, and mainly distributed in Guangxi, Chongqing, Shaanxi, Ningxia, and southern Gansu. 2) The annual maximum value of NDVI (NDVImax) and the growing season average value of NDVI (NDVISeaAvg) temporal variation significantly increased (P<0.001). 3) The annual average precipitation, humidity index, vegetation type, and soil type were the main driving factors for the spatial distribution of NDVImax, with the explanatory powers of 0.776, 0.764, 0.762 and 0.505, respectively. The average annual temperature, physiognomy type, slope, and cumulative temperature (≥10 ℃) served as the secondary factors, with the explanatory powers of above 15%. 4) Each natural factor demonstrated the optimal range to improve vegetation growth. The interaction between different natural factors was bivariate enhancement or nonlinear enhancement. The findings can be helpful to understand the influence of natural factors on vegetation cover changes and its driving mechanism, thereby to provide an important reference for the restoration of damaged vegetation, and sustainable development of ecological environment, further to promote the Silk Road Economic Belt for the better serving the domestic and foreign economic development.